Overview

Dataset statistics

Number of variables12
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.5 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qty_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qty_invoicesHigh correlation
qty_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qty_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qty_items is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qty_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.4442279)Skewed
frequency is highly skewed (γ1 = 24.88037069)Skewed
qt_returns is highly skewed (γ1 = 51.79774426)Skewed
avg_basket_size is highly skewed (γ1 = 44.68328098)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
qt_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-01-18 14:09:26.789508
Analysis finished2023-01-18 14:10:19.611506
Duration52.82 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:19.816895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2023-01-18T11:10:20.234464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.2261
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:20.620981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.491
Coefficient of variation (CV)3.8485342
Kurtosis353.95857
Mean2749.2261
Median Absolute Deviation (MAD)672.72
Skewness16.777879
Sum8162452.2
Variance1.1194678 × 108
MonotonicityNot monotonic
2023-01-18T11:10:21.067491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140438.72 1
< 0.1%
124564.53 1
< 0.1%
117375.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.288649
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:21.480270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756171
Coefficient of variation (CV)1.2094852
Kurtosis2.7780386
Mean64.288649
Median Absolute Deviation (MAD)26
Skewness1.7983969
Sum190873
Variance6046.0221
MonotonicityNot monotonic
2023-01-18T11:10:21.901105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qty_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7228023
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:22.334738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8566539
Coefficient of variation (CV)1.5476079
Kurtosis190.82536
Mean5.7228023
Median Absolute Deviation (MAD)2
Skewness10.766456
Sum16991
Variance78.440319
MonotonicityNot monotonic
2023-01-18T11:10:22.766047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qty_products
Real number (ℝ)

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.70529
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:23.177012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.842
Coefficient of variation (CV)2.1991065
Kurtosis354.83735
Mean122.70529
Median Absolute Deviation (MAD)44
Skewness15.70614
Sum364312
Variance72814.703
MonotonicityNot monotonic
2023-01-18T11:10:23.610009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 45
 
1.5%
20 38
 
1.3%
35 35
 
1.2%
15 33
 
1.1%
29 33
 
1.1%
19 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 29
 
1.0%
Other values (459) 2630
88.6%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 27
0.9%
10 27
0.9%
ValueCountFrequency (%)
7837 1
< 0.1%
5670 1
< 0.1%
5095 1
< 0.1%
4577 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1636 1
< 0.1%

qty_items
Real number (ℝ)

Distinct1665
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.4611
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:24.057092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.4
Q1296
median639
Q31399
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5882.9765
Coefficient of variation (CV)3.6620722
Kurtosis467.15372
Mean1606.4611
Median Absolute Deviation (MAD)420
Skewness17.878445
Sum4769583
Variance34609413
MonotonicityNot monotonic
2023-01-18T11:10:24.492271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
288 8
 
0.3%
272 8
 
0.3%
246 8
 
0.3%
516 7
 
0.2%
394 7
 
0.2%
Other values (1655) 2886
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
79963 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
62812 1
< 0.1%
58243 1
< 0.1%
57785 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.900057
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:24.946929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.974384
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9343
Coefficient of variation (CV)19.979445
Kurtosis2890.7074
Mean51.900057
Median Absolute Deviation (MAD)5.9942223
Skewness53.444228
Sum154091.27
Variance1075232.8
MonotonicityNot monotonic
2023-01-18T11:10:25.355723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.35143
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:25.741239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.928571
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.404762

Descriptive statistics

Standard deviation63.542829
Coefficient of variation (CV)0.94345182
Kurtosis4.8877032
Mean67.35143
Median Absolute Deviation (MAD)26.285714
Skewness2.062909
Sum199966.4
Variance4037.6912
MonotonicityNot monotonic
2023-01-18T11:10:26.159240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
21 17
 
0.6%
46 17
 
0.6%
11 17
 
0.6%
1 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11379122
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:26.553931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049418605
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033078735

Descriptive statistics

Standard deviation0.40815715
Coefficient of variation (CV)3.5868949
Kurtosis989.35782
Mean0.11379122
Median Absolute Deviation (MAD)0.012191338
Skewness24.880371
Sum337.84614
Variance0.16659226
MonotonicityNot monotonic
2023-01-18T11:10:26.936364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.02777777778 17
 
0.6%
0.0625 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2637
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qt_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:27.371447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-01-18T11:10:27.760736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.34954
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:28.191193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172
Q3281.5
95-th percentile599.52
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation791.50241
Coefficient of variation (CV)3.1742686
Kurtosis2256.2455
Mean249.34954
Median Absolute Deviation (MAD)82.75
Skewness44.683281
Sum740318.79
Variance626476.07
MonotonicityNot monotonic
2023-01-18T11:10:28.584019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
71 7
 
0.2%
Other values (1963) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1010
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.155074
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-18T11:10:29.017403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.513033
Coefficient of variation (CV)0.88074783
Kurtosis27.694698
Mean22.155074
Median Absolute Deviation (MAD)8.2
Skewness3.4982521
Sum65778.414
Variance380.75846
MonotonicityNot monotonic
2023-01-18T11:10:29.802166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 40
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
18 31
 
1.0%
10 30
 
1.0%
16 29
 
1.0%
17 28
 
0.9%
Other values (1000) 2622
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

Interactions

2023-01-18T11:10:14.206150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:27.396320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:32.163148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:36.224735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:40.294824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:44.455937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:48.654467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:52.916005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:57.171349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:01.362311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:05.474111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:09.558181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:14.530122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:27.722072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:32.498692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:36.549567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:40.615496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:44.812375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:48.996998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:53.209966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:57.523606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:01.704768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:05.812417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:09.927335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:14.863847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:28.076011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:32.817213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:36.898908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:41.166111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:45.129920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:49.349723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:53.492905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:57.878368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:02.038353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:06.154656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:10.284477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:15.231425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:28.470542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:33.160735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:37.229828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:41.487544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:45.489480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:49.663866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:53.857545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:58.226032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:02.388786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:06.504934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:10.673930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:15.559900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:28.851424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:33.468956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:37.536145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:41.787007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:45.833032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:49.984923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:54.172443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:58.536781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:02.712922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:06.838081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:10.992934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:15.923071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:29.222685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:33.812290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:37.854882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:42.129221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:46.200083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:50.364385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:54.760154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:58.908670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:03.075695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:07.159312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:11.665504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:16.273736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:30.131175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:34.169003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:38.205075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:42.458373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:46.575274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:50.756980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:55.108254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:59.264698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:03.442133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:07.493961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:12.026477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:16.619555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:30.448847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:34.525585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:38.534550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:42.765206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:46.915992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:51.094232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:55.446554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:59.609405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:03.792243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:07.847895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:12.366659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:16.972256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:30.772632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:34.879127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:38.901232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:43.096054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:47.279231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:51.461561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:55.791551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:59.962194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:04.095545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:08.177473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:12.728710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:17.334936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:31.114534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:35.211730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:39.246769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:43.420844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:47.630438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:51.832428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:56.162749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:00.295825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:04.423360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:08.527209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:13.086911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:17.679612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:31.478734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:35.544286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:39.614388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:43.768379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:48.002658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:52.185800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:56.498808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:00.647711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:04.766360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:08.875961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:13.459319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:18.049155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:31.811466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:35.878760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:39.970837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:44.118803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:48.332234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:52.535884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:09:56.844635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:01.015906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:05.118012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:09.214855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-18T11:10:13.855628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-18T11:10:30.151563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysqty_invoicesqty_productsqty_itemsavg_ticketavg_recency_daysfrequencyqt_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0760.0010.0250.013-0.070-0.1310.019-0.002-0.063-0.123-0.007
gross_revenue-0.0761.000-0.4150.7700.7440.9270.246-0.2480.0900.3720.5760.291
recency_days0.001-0.4151.000-0.502-0.435-0.4080.0480.1080.018-0.119-0.098-0.106
qty_invoices0.0250.770-0.5021.0000.6900.7170.059-0.2580.0780.2930.1010.025
qty_products0.0130.744-0.4350.6901.0000.731-0.377-0.1660.0360.2420.3840.699
qty_items-0.0700.927-0.4080.7170.7311.0000.168-0.2270.0800.3440.7290.321
avg_ticket-0.1310.2460.0480.059-0.3770.1681.000-0.1220.0910.1900.189-0.611
avg_recency_days0.019-0.2480.108-0.258-0.166-0.227-0.1221.000-0.881-0.396-0.0770.048
frequency-0.0020.0900.0180.0780.0360.0800.091-0.8811.0000.2340.027-0.072
qt_returns-0.0630.372-0.1190.2930.2420.3440.190-0.3960.2341.0000.2110.019
avg_basket_size-0.1230.576-0.0980.1010.3840.7290.189-0.0770.0270.2111.0000.448
avg_unique_basket_size-0.0070.291-0.1060.0250.6990.321-0.6110.048-0.0720.0190.4481.000

Missing values

2023-01-18T11:10:18.558594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-18T11:10:19.270977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqty_invoicesqty_productsqty_itemsavg_ticketavg_recency_daysfrequencyqt_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.0297.01733.018.15222235.50000017.00000040.050.9705888.735294
1130473232.5956.09.0171.01390.018.90403527.2500000.02830235.0154.44444419.000000
2125836705.382.015.0232.05028.028.90250023.1875000.04032350.0335.20000015.466667
313748948.2595.05.028.0439.033.86607192.6666670.0179210.087.8000005.600000
415100876.00333.03.03.080.0292.0000008.6000000.07317122.026.6666671.000000
5152914623.3025.014.0102.02102.045.32647123.2000000.04011529.0150.1428577.285714
6146885630.877.021.0327.03621.017.21978618.3000000.057221399.0172.42857115.571429
7178095411.9116.012.061.02057.088.71983635.7000000.03352041.0171.4166675.083333
81531160767.900.091.02379.038194.025.5434644.1444440.243316474.0419.71428626.142857
9160982005.6387.07.067.0613.029.93477647.6666670.0243900.087.5714299.571429
customer_idgross_revenuerecency_daysqty_invoicesqty_productsqty_itemsavg_ticketavg_recency_daysfrequencyqt_returnsavg_basket_sizeavg_unique_basket_size
5627177271060.2515.01.066.0645.016.0643946.01.0000006.0645.00000066.0
563717232421.522.02.036.0203.011.70888912.00.1538460.0101.50000018.0
563817468137.0010.02.05.0116.027.4000004.00.4000000.058.0000002.5
564913596697.045.02.0166.0406.04.1990367.00.2500000.0203.00000083.0
5655148931237.859.02.073.0799.016.9568492.00.6666670.0399.50000036.5
565912479473.2011.01.030.0382.015.7733334.01.00000034.0382.00000030.0
568014126706.137.03.015.0508.047.0753333.00.75000050.0169.3333335.0
5686135211092.391.03.0435.0733.02.5112414.50.3000000.0244.333333145.0
569615060301.848.04.0120.0262.02.5153331.02.0000000.065.50000030.0
571512558269.967.01.011.0196.024.5418186.01.000000196.0196.00000011.0